Revealing the Dynamic Roles of Splicing and Translation in Gene Regulation

A single genome produces the huge diversity of cells and tissues needed to make a human by regulating gene expression to turn on and off the right genes at the right times. The final, post-transcriptional steps of gene expression  RNA processing and translation  are essential to the proper outcome. My goal is to understand what the cell achieves by adding extra layers of regulation at these final steps. I develop and use computational and high-throughput experimental methods to understand post-transcriptional regulation as a dynamic system that can be modeled and manipulated.

One puzzle that has captivated biologists is the biased use of different synonymous codons to encode proteins. We have recently used neural networks to model the movement of ribosomes along mRNAs, showing how different codon choices determine ribosome speed. We applied our model to design synonymous variants of a fluorescent protein spanning the range of possible translation speeds predicted with our model. We found that levels of the fluorescent protein in yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo, that we can harness this information to design coding sequences, and that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output. Our research will build on this to decode the hidden layer of regulation arising from codon choice and understand how the cell uses this to ensure the proper output of each gene.